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6th International Conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2022

作者： Utama, Ditdit Nugeraha Bina Nusantara University Computer Science Department BINUS Graduate Program - Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

Membership function (MF) in process of fuzzy logic is very meaningful. It depicts the core of model. It can be adopted from the expert judgment and also coming from the configuration of data behavior. The study is an experimental research by analyzing time-series data behavior in developing the MF by using the programming language Python. The MF academically produced called as floating MF (FMF). Such a FMF operated to realize the fuzzy logic conception in an evaluation model. The constructed model is a model in simply measuring the student's performance. By using the ten series-data of student's grade point average (GPA) and absence per annual (APS) values, the model can simulate the moving students' performance per semester. When compared to the conventional FL based model, the proposed model has a similarity average 85%. © 2022 IEEE.

关键词： evaluation model floating fuzzy logic moving performance value student performance

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BERT base model for toxic comment analysis on Indonesian social media 7

BERT base model for toxic comment analysis on Indonesian soc...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Nabiilah, Ghinaa Zain Prasetyo, Simeon Yuda Izdihar, Zahra Nabila Girsang, Abba Suganda Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897. © 2022 Elsevier B.V.. All rights reserved.

关键词： Social networking (online)

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Spatiotemporal Water Quality Prediction Using Graph Neural Networks Based on Diffusion Decay Partial Differential Equations 9

Spatiotemporal Water Quality Prediction Using Graph Neural N...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Jia, Lei Yen, Neil Pei, Yan Graduate School of Computer Science University of Aizu Fukushima Japan

ISBN: (纸本)9798350394191

Complex environmental geographical spaces exhibit characteristics such as nonlinearity, multiscale nature, and uncertainty, representing typical spatiotemporal dynamic systems. With the advent of big data, data-driven spatiotemporal prediction models have emerged. However, the high-dimensional complexity of spatiotemporal phenomena makes these models difficult to interpret. This paper proposes a river network feature identification architecture based on coupled dynamic partial differential diffusion equations and graph neural networks. By deeply analyzing the solving process of hydrodynamic-diffusion equations and graph neural networks, spatial and nonlinear information of the environmental field is embedded into the graph network. We design a message-passing network based on an aggregation sampling method for flow field information fusion, allowing multiscale flow field information to be propagated, aggregated, and updated to predict the spatiotemporal evolution of water quality. The proposed model characterizes the constraint relationship between the rate of change of water quality concentration in time and space dimensions, enhancing the model's physical consistency and interpretability. © 2024 IEEE.

关键词： Graph neural networks

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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The Use of Deep and Machine Learning for Face Expression Recognition: A Literature Review 7

The Use of Deep and Machine Learning for Face Expression Rec...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Pangestu, Gusti Hendric Spits Warnars, Harco Leslie Soewito, Benfano Gaol, Ford Lumban BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the current era, Artificial Intelligence (AI) and computer Vision take the main role and participating to the people daily activities. Face Expression became as interesting topic to be explore. Face expression recognition or detection could be applied in many aspects, such as for student focus detection based on face expression, intruder detection system, lie detection and many more. Referring the useful of face expression detection, there are also much research that focused on the methodology to detects or recognize the face expression. Several approaches are use such by using Deep Learning, Machine Learning and statistical method. This paper focused on the process to obtain and knowing the best approach or methods to recognize the face expression by using Systematic Literature Review (SLR) process. From hundreds of retrieved papers with similar interest, we do the selection based on the interest, scope, and topic similarity. The process keeps repeating until we found papers that has similarity criteria in output, dataset, subject and the methods. There are 6 papers in total that fulfil our requirements based on subject, output, result and methods. Subsequently, every research paper was deep-reviewed and compared with another to find the best approaches proposed and dataset used. © 2022 IEEE.

关键词： Intrusion detection

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Interaction through bionic signals. Overview 24

Interaction through bionic signals. Overview

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Ralev, Ivan Krastev, Georgi University of Ruse Department of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798331515799

The paper examines the possibilities of bionic technology as a means of communication with a computer system. A classification of the most commonly used signal detection technologies is carried out. A model of a system using this technology is presented. An overview of the possibilities of the application of such systems in the daily life of the mass consumer has been carried out. © 2025 IEEE.

关键词： Human computer interaction

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Clustering Preachers and Mosques for Effective Religious Outreach in Kampar Using DHC-PCA Algorithm 2

Clustering Preachers and Mosques for Effective Religious Out...

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2nd IEEE International Conference on Electrical Engineering, computer and Information Technology, ICEECIT 2024

作者： Kurniawan, Rahmad Surya, Natania Adela Fatayat Mahdiyah, Evfi Husti, Ilyas Arisman Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331504373

Preacher assignments to mosques in Kampar Regency have often been inefficient due to geographical distance and compatibility issues, limiting effective religious outreach. This study aims to optimize preacher assignments by applying the Divisive Hierarchical Clustering (DHC) algorithm. Analysis was conducted on a dataset of 250 records, clustering preachers and mosques based on geographical proximity and relevant attributes. Initial clustering modelling produced the highest silhouette score of 0.336. Dimensionality reduction using Principal Component Analysis (PCA) significantly improved the silhouette score to 0.8197, reflecting enhanced clustering quality. The elbow method validated eight clusters as optimal, with a value of 292.33. Research findings demonstrate that preachers can be effectively placed in geographically closer mosques aligned with their profiles, reducing inefficiencies. Business intelligence techniques visualized the clustering results, providing decision-makers with insights into geographical distribution and facilitating improved assignment strategies. These findings hold substantial implications for optimizing religious outreach in Kampar by minimizing logistical challenges and increasing engagement effectiveness. © 2024 IEEE.

关键词： Principal component analysis

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection 12th

The Effect of Statistical Hypothesis Testing on Machine Le...

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12th Brazilian Conference on Intelligent systems, BRACIS 2023

作者： Gonçalves, Marcel Chacon Silva, Rodrigo Graduate Program on Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil

ISBN: (纸本)9783031453885

Statistical tests of hypothesis play a crucial role in evaluating the performance of machine learning (ML) models and selecting the best model among a set of candidates. However, their effectiveness in selecting models over larger periods of time remains unclear. This study aims to investigate the impact of statistical tests on ML model selection in sequential experiments. Specifically, we examine whether selecting models based on statistical tests leads to higher quality models after a significant number of iterations and explore the effect of the number of tests performed and the preferred statistical test for different experimental time horizons. The study on binary classification problems reveals that the use of statistical tests should be approached with caution, particularly in challenging scenarios where generating improved models is difficult. The analysis demonstrates that statistical tests may impede progress and impose overly stringent acceptance criteria for new models, hindering the selection of high-quality models. The findings also indicate that the dominance of versions without statistical tests remained consistent, suggesting the need for further research in this area. Although this study is limited by the number of datasets and the absence of pre-test assumption verification, it emphasizes the importance of understanding the impact of statistical tests on ML model selection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Comprehensive Sentiment Analysis of ChatGPT App Reviews Using Lexicon-Based Methods, Deep Learning, and Machine Learning

Comprehensive Sentiment Analysis of ChatGPT App Reviews Usin...

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computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Dharmawan Elmadya Tanty Oktavia Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

In the current era, many AI chatbot applications are widely used by people. One of the most popular among these is ChatGPT. Due to its application-based platform, ChatGPT has received a large volume of user feedback. This study aims to analyze these user comments through sentiment analysis with also implementing a combination of machine learning and deep learning models. For traditional machine learning the models are such as Random Forest, Support Vector Machine, Decision Tree, Naive Bayes, XGBoost, and CatBoost. Meanwhile, for the deep learning this study implementing BERT, RoBERTa, XLNet, and GPT- 2. The dataset used in this study is qualitative and was sourced from Google Play Web. Each of the model will perform and evaluated by the accuracy, precision, F1-Score, and recall. This study finds that the GPT-2 model, as one of the deep learning approaches, provided the best performance in this study because can prove excellent performance, efficiency, effectiveness and accuracy compared to other existing models

关键词： Deep learning Support vector machines Analytical models Sentiment analysis Technological innovation Accuracy Computational modeling Chatbots Transformers Data models

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